@tiger and I are trying to contribute back to the excellent bayesian methods for hackers book with a pyro implementation and we’re running into some confusing behavior around pyro.sample

outside of a model:

```
cheating_frequency = pyro.sample('cheating_frequency', dist.Uniform(0, 1))
print(cheating_frequency) # => tensor(0.3947) This makes sense
```

but with our model code we get a different return type with this same sample

```
N = 100
n_steps = 1
data = torch.cat((torch.ones(35, 1), torch.zeros(65, 1)))
def model(data):
cheating_frequency = pyro.sample(
'cheating_frequency',
dist.Uniform(0, 1)
)
print(cheating_frequency) #=> tensor([[ 1.3497]]) This does not make sense???
true_answers = dist.Bernoulli(probs=cheating_frequency).sample((100,))
first_coin_flips = dist.Bernoulli(probs=0.5).sample((100,))
second_coin_flips = dist.Bernoulli(probs=0.5).sample((100,))
observed_trues = first_coin_flips * true_answers + ( 1- first_coin_flips) * second_coin_flips
observed_proportion = observed_trues.sum() / N
for i in range(len(data)):
pyro.sample("obs_{}".format(i), dist.Bernoulli(probs=observed_proportion), obs=data[i])
def guide(data):
mean = pyro.param('guide_mean', torch.randn(1, 1))
stdev = pyro.param('guide_stdev', torch.randn(1, 1))
cheating_frequency = pyro.sample(
'cheating_frequency',
dist.Normal(mean, stdev)
)
# setup the optimizer
adam_params = {"lr": 0.0005, "betas": (0.90, 0.999)}
optimizer = Adam(adam_params)
# setup the inference algorithm
svi = SVI(model, guide, optimizer, loss=Trace_ELBO())
# do gradient steps
for step in range(n_steps):
svi.step(data)
if step % 100 == 0:
print('.', end='')
```

The `print(cheating_frequency)`

statement returns `tensor([[ 1.3497]])`

There are two things that are confusing here:

- The cheating frequency type is a 2 dimensional tensor
`tensor([[ 1.3497]])`

instead of a scalar. - The value seems like it was sampled from the guide’s normal distribution, not the uniform distribution from [0,1] in the model

The documentation says the following about pyro.sample:

Pyro’s backend uses these names to uniquely identify sample statements and change their behavior at runtime depending on how the enclosing stochastic function is being used.

Is this an example of pyro changing the behavior of `pyro.sample`

at runtime given the model context? Is pyro changing both the return type *and* the distribution that it’s sampling from?